4.16 — Water & utilities (leak and night flows)
- Scenario. Thin denominators at night trigger spurious leak alarms.
- Inputs. Loss/throughput confidence per district meter.
- Behavior. Ratios remain visible; confidence tiers alerts instead of flipping thresholds.
- Week-1 pilot. Band alarms; review night-time alerts for reductions; keep thresholds unchanged.
- KPIs & acceptance. Fewer false leak tickets; faster real-leak identification.
- Calculator-fast example.
Given(a_loss, a_through) = (+0.5, +0.2)a_div := (a_loss - a_through) / (1 - a_loss*a_through) = 0.3333333333
Effect. Value is unchanged; anA0/A+band ona_div ≈ +0.33signals fragile evidence during low-flow hours.
4.17 — Additive manufacturing (print stability)
- Scenario. Risky decisions under weak evidence drive stringing/warping.
- Inputs. Nozzle temp, flow, and vision cues with alignments.
- Behavior. Pipelines drop low-confidence branches first; values remain classical.
- Week-1 pilot. Publish the lane; apply advisory smoothing on feed-forward only.
- KPIs & acceptance. Less stringing/warping; lower scrap rate.
- Calculator-fast example.
Two strong cues:a_temp = +0.5,a_flow = +0.5a_fused := tanh( atanh(0.5) + atanh(0.5) ) = 0.8
Policy hint. Usek := 1 - |a_fused| = 0.2to gently reduce aggressive moves when confidence dips.
4.18 — AR/VR & XR (pose stability)
- Scenario. Occlusions induce jumps and user discomfort.
- Inputs. IMU, camera, magnetometer confidences; pose value stays classical.
- Behavior. Blend by confidence; glide through occlusions; keep numbers visible.
- Week-1 pilot. Surface lane in a dev HUD; enable advisory smoothing only.
- KPIs & acceptance. Lower pose jerk; better session quality scores.
- Calculator-fast example (weighted fuse).
a_IMU = +0.7 (w=2),a_cam = +0.3 (w=1)U := 2*atanh(0.7) + 1*atanh(0.3);W := 3;a_out := tanh( U / max(W, 1e-12) ) ≈ 0.59
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